Postsynthetic modification (PSM) has been demonstrated to be a powerful method for achieving new covalent organic frameworks (COFs) via single-step or multistep organic functional group ...transformations on established COF frameworks. PSM, however, might sometimes lead to collapse of the COF framework, decreases in crystallinity, or low postsynthetic yield due to the inherent limit of solid-state synthesis. Herein we report, for the first time, a new synthetic strategy that can generate new COFs via multicomponent one-pot in situ reactions. In total, 12 α-aminonitrile- and quinoline-linked COFs with high crystallinity and permanent porosity are successfully achieved by three-component one-pot in situ Strecker and Povarov reactions under solvothermal conditions in high yields. The obtained COFs feature the same structures as those obtained from the stepwise PSM approach on an established imine-linked COF. This in situ multicomponent assembly strategy, as a synthetic methodology parallel to PSM, might open a new route for constructing COFs that is not possible under PSM conditions.
Drug repositioning, meanings finding new uses for existing drugs, which can accelerate the processing of new drugs research and development. Various computational methods have been presented to ...predict novel drug-disease associations for drug repositioning based on similarity measures among drugs and diseases. However, there are some known associations between drugs and diseases that previous studies not utilized. In this work, we develop a deep gated recurrent units model to predict potential drug-disease interactions using comprehensive similarity measures and Gaussian interaction profile kernel. More specifically, the similarity measure is used to exploit discriminative feature for drugs based on their chemical fingerprints. Meanwhile, the Gaussian interactions profile kernel is employed to obtain efficient feature of diseases based on known disease-disease associations. Then, a deep gated recurrent units model is developed to predict potential drug-disease interactions. The performance of the proposed model is evaluated on two benchmark datasets under tenfold cross-validation. And to further verify the predictive ability, case studies for predicting new potential indications of drugs were carried out. The experimental results proved the proposed model is a useful tool for predicting new indications for drugs or new treatments for diseases, and can accelerate drug repositioning and related drug research and discovery.
Protein-protein interactions (PPIs) play an important role in most of the biological processes. How to correctly and efficiently detect protein interaction is a problem that is worth studying. ...Although high-throughput technologies provide the possibility to detect large-scale PPIs, these cannot be used to detect whole PPIs, and unreliable data may be generated. To solve this problem, in this study, a novel computational method was proposed to effectively predict the PPIs using the information of a protein sequence. The present method adopts Zernike moments to extract the protein sequence feature from a position specific scoring matrix (PSSM). Then, these extracted features were reconstructed using the stacked autoencoder. Finally, a novel probabilistic classification vector machine (PCVM) classifier was employed to predict the protein-protein interactions. When performed on the PPIs datasets of
Yeast
and
H. pylori
, the proposed method could achieve average accuracies of 96.60% and 91.19%, respectively. The promising result shows that the proposed method has a better ability to detect PPIs than other detection methods. The proposed method was also applied to predict PPIs on other species, and promising results were obtained. To evaluate the ability of our method, we compared it with the-state-of-the-art support vector machine (SVM) classifier for the
Yeast
dataset. The results obtained
via
multiple experiments prove that our method is powerful, efficient, feasible, and make a great contribution to proteomics research.
Protein-protein interactions (PPIs) play an important role in most of the biological processes.
Solid base synthesized from SBC ash for biodiesel production from waste oils with 8 cycles and anti-saponification. It was further magnetized for easy separation.
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•Direct production ...of biodiesel at 65 °C from waste oil by solid base was achieved.•Solid base from alkalized SBC ash had basic sites from Na2SiO3 and Na2SiAlO4.•Biodiesel from soybean oil reached >99% yield with 8 cycles (>95%).•Biodiesel yield was 96% from SBC oil (AV 10) at 65 °C (vs. 120 °C with H2SO4).•It was magnetized for separation with biodiesel yield 99% for 3 cycles (87%).
Biodiesel was directly one-step produced from waste oils without pretreatment catalyzed by a solid base alkalized from spent bleaching clay (SBC) ash. Optimized conditions were obtained with 99.1% biodiesel yield from soybean oil with an orthogonal design. The base catalyst was stable within 8 cycles (>95% biodiesel yield) and resistant to saponification (AV = 9.7 mg KOH/g, 96.5% biodiesel yield). The base was characterized with XRD, EDX-mapping, FT-IR, XRF and TPD, and it had similar strong basicity to Na2SiO3 (0.21 vs. 0.22 mmol/g for Na2SiO3) with active sites of Na2O and CH3ONa evolved from Na2SiO3 and NaAlSiO4 by reactions of NaOH with oxides (e.g., SiO2, Al2O3) in SBC ash. Furthermore, the base was magnetized with magnetism of 6.86 emu/g by carbonizing residual oil in SBC as carbon support and reductant (of Fe2O3 to magnetic Fe3O4 particles). It catalyzed soybean oil to produce biodiesel with 99.2% yield and blended oil (AV = 5.9) to biodiesel with 91.9% yield without any saponification. The catalyst was magnetically separated and reused for 3 cycles with 87% yield. The non-magnetic base could also efficiently catalyze actual SBC oil for the production of biodiesel with 95% yield at AV of 10. This work realized the full use of inorganics in SBC, and its oil for direct biodiesel production at a low temperature (i.e., 65 vs. 120 °C with sulfuric acid process) without wastes produced and results can easily find practical applications for waste oils.
Embryonic stem cells (ESCs) repress the expression of exogenous proviruses and endogenous retroviruses (ERVs). Here, we systematically dissected the cellular factors involved in provirus repression ...in embryonic carcinomas (ECs) and ESCs by a genome-wide siRNA screen. Histone chaperones (Chaf1a/b), sumoylation factors (Sumo2/Ube2i/Sae1/Uba2/Senp6), and chromatin modifiers (Trim28/Eset/Atf7ip) are key determinants that establish provirus silencing. RNA-seq analysis uncovered the roles of Chaf1a/b and sumoylation modifiers in the repression of ERVs. ChIP-seq analysis demonstrates direct recruitment of Chaf1a and Sumo2 to ERVs. Chaf1a reinforces transcriptional repression via its interaction with members of the NuRD complex (Kdm1a, Hdac1/2) and Eset, while Sumo2 orchestrates the provirus repressive function of the canonical Zfp809/Trim28/Eset machinery by sumoylation of Trim28. Our study reports a genome-wide atlas of functional nodes that mediate proviral silencing in ESCs and illuminates the comprehensive, interconnected, and multi-layered genetic and epigenetic mechanisms by which ESCs repress retroviruses within the genome.
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•Genome-wide siRNA screen identifies key determinants for proviral silencing in ESCs•Histone chaperones, sumoylation factors, and chromatin modifiers can repress ERVs•Sumo2 orchestrates viral silencing through sumoylation modification of Trim28•Chaf1a regulates provirus and ERVs via its interaction with Eset, Kdm1a, and Hdac1/2
Proviral silencing is a characteristic of the pluripotent state, and the precise expression of endogenous retrovirus is critical for embryogenesis and development. In this study, a genome-wide siRNA screen identifies new cellular factors and new mechanisms involved in retroviral repression in embryonic stem cells.
An inverse model based on the shooting method, Mie theory and the improved Kramers–Kronig (KK) relation was combined with FTIR and Abbe refractometer measurements to calculate the complex refractive ...indices of various infrared opacifiers. The effects of opacifier sizes, types and shapes were then analyzed based on the Rosseland mean extinction coefficient using Mie theory and anomalous diffraction theory (ADT). This model provides theoretical guidelines for designing materials with optimized parameters, such as size, type and shape of opacifiers, to improve the aerogel thermal insulation at high temperatures. The results show that the optimum diameter of SiC particles to minimize the radiation is 4 μm for T < 400 K and 3 μm for T > 400 K. Carbon black is the optimum opacifier for T < 600 K while SiC is the optimum opacifier to minimize the radiative heat transfer for T > 600 K among the investigated opacifiers of SiC, TiO2, ZrO2, amorphous SiO2 and carbon black. The infrared extinction ability for various shapes is largest for oblate spheroids and decreases for spheres, cubes, cylinders with small length-to-diameter ratios, and then long, thin cylinders.
•We present a new inverse model to obtain the optical properties of opacifiers.•The model is based on a shooting method and FTIR and Abbe refractometer measurements.•Effects of opacifier sizes, types and shapes on radiative properties are analyzed.•Theoretical guidelines are provided for opacifier designs with optimum parameters.•Optimized parameters of opacifiers can improve silica aerogel thermal insulations.
To investigate the neuroprotective effects of insulin on diabetic encephalopathy and its mechanism.
The diabetic model was established by injection of streptozotocin. Behavior examinations were ...conducted by the Morris water maze. Histopathological alterations were detected by HE staining. ROS, CAT levels and SOD activity were measured using a microplate reader. In vitro, the viability of wild type and knock-down PC12 cells was detected by MTT assay, the morphology of cells was monitored under a microscope. The subcellular distribution of Nrf2 was observed by western blotting and immunohistochemistry.
Evident oxidative stress injury was observed in diabetic rats and H2O2-induced PC12 cells. Insulin not only protect diabetic rat from oxidative stress injury but also significantly inhibited H2O2-induced apoptosis and intracellular ROS in cells. In addition, the level of malondialdehyde was reduced, and the activities of superoxide dismutase, catalase and glutathione peroxidase were augmented in both diabetic rats and PC12 cells. Interestingly, insulin promoted the translocation of Nrf2 into the nucleus and activation of downstream antioxidant protein expression. Further, the Nrf2 knockdown cells suffered more serious H2O2-induced damage than the wild PC12 cells. Moreover, insulin had no significant protective effect on knockdown cells with H2O2-damage.
Collectively, our results suggested that insulin significantly inhibited neuronal damage through the Nrf2 signaling pathway, which regulates endogenous oxidant-antioxidant balance, therefore, insulin may be a potential protective agent for the treatment of oxidative stress-induced diabetic encephalopathy.
Objectives
To identify CT markers for screening of early type 2 diabetes and assessment of the risk of incident diabetes using a radiomics method.
Methods
The medical records of 26,947 inpatients ...were reviewed. A total of 690 patients were selected and allocated to a primary cohort, a validation cohort, and a prediction cohort and used to build prediction models for diabetes. Three radiomics signatures were constructed using CT image features extracted from three regions of interest, i.e., in the pancreas, liver, and psoas major muscle. By incorporating radiomics signatures and other markers, we built a radiomics nomogram that could be used to screen for early diabetes and predict future diabetes.
Results
Of the three abdominal organs for which radiomics signature were constructed, that of the pancreas showed the best discriminatory power for early diabetes screening and prediction (C-statistics of 0.833, 0.846, and 0.899 for the primary cohort, validation cohort, and prediction cohort, respectively). The sensitivity and specificity of the nomogram for prediction of 3-year incident diabetes were 0.827 and 0.807, respectively.
Conclusions
This study presents alternative radiomics markers that have potential for use in screening for undiagnosed type 2 diabetes and prediction of 3-year incident diabetes.
Key Points
•
CT images may provide useful information to evaluate the risk of developing diabetes.
• Radiomics score for diabetes prediction is based on subtle changes of abdominal organs detected by CT.
• The radiomics signature of pancreas, a combination of five features of CT images, is efficient for early diabetes screening and prediction of future diabetes (AUC > 0.8).
Considering the basic concepts of P‐type and N‐type originated from inorganic semiconductors, it has been deduced in this report that the non‐doped polymer semiconductors should be ambipolar ...materials. Thus, the carrier dynamics of hole‐only and electron‐only devices of poly(N‐vinylcarbazole) (PVK) are researched through impedance spectroscopy. The results show that the hole and electron transports both possess high dispersion with parameter of 0.1–0.2 and 0.1, respectively. In addition, the hole and electron mobilities under different electric field are basically in the same order of magnitude, which shows that PVK is one intrinsically ambipolar polymer semiconductor although it is traditionally thought to “P‐type.” Finally, the abnormal negative slope phenomenon of transferring curve is analyzed, which is ascribed to the energy relaxation through vibration in polymer segment.
This paper uses impedance spectroscopy to study the carrier dynamics of non‐doped “P‐type” poly(N‐vinylcarbazole). The hole and electron mobilities of poly(N‐vinylcarbazole) are almost same to each other under various film thickness conditions. In addition, the mobilities are negatively dependent on the electric field, which is attributed to the energy relaxation through vibration of polymer segment.
A physics-based compact model of metal-oxide-based resistive-switching random access memory (RRAM) cell under dc and ac operation modes is presented. In this model, the conductive filament evolution ...corresponding to the resistive switching process is modeled by considering the transport behaviors of oxygen vacancies and oxygen ions together with the temperature effect. Both the metallic-like and electron hopping conduction transports are considered to model the conduction of RRAM. The model can reproduce both the typical I-V characteristics of RRAM in high-/low-resistance state (LRS) and the nonlinear characteristics in LRS. Moreover, to accurately model ac operation mode, the effects of parasitic capacitance and resistance are included in our model. The developed compact model is verified and calibrated by measured data in different HfO x -based RRAM devices under dc and ac operation modes. The excellent agreement between the model predictions and experimental results shows a promising prospect of the future implementation of this compact model in large-scale circuit simulation to optimize the design of RRAM.
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